# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import mindspore.context as context
import mindspore.nn as nn
import numpy as np
from mindspore.common import dtype as mstype

from mindspore import Tensor
from mindspore import dtype
from mindspore.ops import operations as P
from tests.mark_utils import arg_mark

context.set_context(mode=context.GRAPH_MODE, device_target="CPU")


class NetExpm1(nn.Cell):
    def __init__(self):
        super(NetExpm1, self).__init__()
        self.expm1 = P.Expm1()

    def construct(self, x):
        return self.expm1(x)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='essential')
def test_expm1_dshape():
    """
    Feature: Test expm1 dynamic shape.
    Description: Test expm1 dynamic shape.
    Expectation: Success.
    """
    net = NetExpm1()
    input_x_dyn = Tensor(shape=[3, None], dtype=mstype.float32)
    net.set_inputs(input_x_dyn)
    input_x = Tensor(np.random.random(([3, 10])), dtype=mstype.float32)
    output = net(input_x)
    expect_shape = (3, 10)
    assert output.asnumpy().shape == expect_shape


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='essential')
def test_expm1_op():
    x = np.random.rand(3, 8).astype(np.float32)
    y = np.random.rand(3, 8).astype(np.float16)

    expm1 = NetExpm1()
    output_x = expm1(Tensor(x, dtype=dtype.float32))
    expect_x = np.expm1(x)
    tol_x = 1e-6
    assert (np.abs(output_x.asnumpy() - expect_x) < tol_x).all()

    output_y = expm1(Tensor(y, dtype=dtype.float16))
    expect_y = np.expm1(y)
    tol_y = 1e-3
    assert (np.abs(output_y.asnumpy() - expect_y) < tol_y).all()


def test_expm1_tensor_api():
    """
    Feature: test expm1 tensor API.
    Description: testcase for expm1 tensor API.
    Expectation: the result match with expected result.
    """
    x = Tensor(np.array([0.0, 1.0, 2.0, 4.0]), mstype.float32)
    output = x.expm1()
    expected = np.array([0., 1.718282, 6.389056, 53.598152])
    np.testing.assert_array_almost_equal(output.asnumpy(), expected, decimal=4)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='essential')
def test_expm1_tensor_modes():
    """
    Feature: test expm1 tensor API in PyNative and Graph modes.
    Description: test case for expm1 tensor API.
    Expectation: the result match with expected result.
    """
    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
    test_expm1_tensor_api()
    context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
    test_expm1_tensor_api()
